tailieunhanh - Sensor and Data Fusion_2

Data fusion is a research area that is growing rapidly due to the fact that it provides means for combining pieces of information coming from different sources/sensors, resulting in ameliorated overall system performance (improved decision making, increased detection capabilities, diminished number of false alarms, improved reliability in various situations at hand) with respect to separate sensors/sources. Different data fusion methods have been developed in order to optimize the overall system output in a variety of applications for which data fusion might be useful: security (humanitarian, military), medical diagnosis, environmental monitoring, remote sensing, robotics. Generally speaking, there is no fusion approach that works better than the others, but depending. | 14 Updating Scarce High Resolution Images with Time Series of Coarser Images a Bayesian Data Fusion Solution Dominique Fasbender1 Valérie Obsomer1 2 Patrick Bogaert1 and Pierre Defourny1 1Dpt of Environmental Sciences and Land Use Planning Université catholique de Louvain 2Institute of Tropical Medecine Antwerpen Belgium 1. Introduction As a consequence of the great variability between sensors the characteristics of remotely sensed data widely differ with respect to spectral and spatial resolutions. Additionally to their respective technical characteristics and peculiarities sensors also have different temporal frequencies of acquisition. Coarser sensors . SPOT VEGETATION or TERRA MODIS have generally close to daily acquisition rates while high spatial resolution sensors . SPOT HRVIR or IKONOS have lower acquisition rates. Cloud-free high resolution imagery may therefore not be available at the required period unlike coarser resolution images. On top of this high resolution images are sometimes so highly priced that updating past high resolution images with recent coarse images can be cost effective. For these reasons there is a real need for a sound theoretical framework that aims at merging information coming from two or more different sensors while taking explicitly into account the spatial resolution discrepancies between images. Typically for cost effective applications this could involve predicting a high resolution image by updating a past one with more recent but coarser images. It is a common fact that remote sensors have different spatial resolution. This change of resolution is thus a typical issue in remote sensing applications. Depending on users needs and the heterogenity of the study areas different algorithms of fusion were proposed for the spatial enhancement of remotely sensed images. These include Brovey method Pohl van Genderen 1998 Intensity-Hue-Saturation IHS Harrison Jupp 1990 Principal Component Analysis PCA Pohl van Genderen 1998 .

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